Development and validation of a CIMP-associated prognostic model for hepatocellular carcinoma

Unmasking Disparities in Gallbladder Cancer Outcomes in the Disaggregated Asian American Population

BACKGROUND

Gallbladder cancer (GBC) is associated with a high mortality rate. Asian American (AsA) are among the fastest-growing populations in the United States, yet little is known about the disparity of GBC within this cohort. This study identified trends in treatment and outcomes for GBC in a disaggregated fashion, specifically for this population.

METHODS

A retrospective analysis of the National Cancer Database (NCDB) between 2010 and 2019 examining all patients treated for gallbladder cancer was performed. Basic demographic factors were identified for patients of Caucasian, African American, and disaggregated Asian subpopulations. Survival curves were used to identify differences in median overall survival, and a multivariate analysis was performed to determine which factors impact overall survival.

RESULTS

A total of 1317 (5%) patients were of AsA origin. Median survival for the overall AsA population is 15.1 months compared with Caucasian (11.5 months) and African Americans (11.4 months) (p < 0.0001). Within the AsA groups, the Korean subpopulation had the lowest survival at 12.6 months, whereas Filipinos had the longest survival at 19.1 months (p < 0.0001). Patients of Filipino descent had the highest rate of surgical resection but lower chemotherapy utilization. Conversely, Korean patients had the highest utilization of multimodality therapy. Multivariate analysis demonstrated that belonging to Chinese, Filipino, or Indian ethnicity was associated with decreased risk of mortality.

CONCLUSIONS

There are disparate differences in survival for patients with GBC between AsA groups. Socioeconomic, genetic, and epigenetic factors may influence these differences. Further research is needed to delineate the causes of this disparity.

Eosinophil-Associated Genes are Potential Biomarkers for Hepatocellular Carcinoma Prognosis

Background: Eosinophils, a type of white blood cell originating from the bone marrow, are widely believed to play a crucial role in inflammatory processes, including allergic reactions and parasitic infections. However, the relationship between eosinophils and liver cancer is not well understood. Methods: Tumor immune infiltration scores were calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Key modules and hub genes associated with eosinophils were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Univariate and multivariate Cox analyses, along with LASSO regression, were used to identify prognostic genes and create a risk model. The Tumor Immune Dysfunction and Exclusion (TIDE) score was used to evaluate the immunotherapeutic significance of the eosinophil-associated gene risk score (ERS) model. Experiments such as flow cytometry, immunohistochemical analysis, real-time quantitative PCR (RT-qPCR), and Western blotting were used to determine gene expression levels and the status of eosinophil infiltration in tumors. Results: A risk trait model including 4 eosinophil-associated genes (RAMP3, G6PD, SSRP1, PLOD2) was developed by univariate Cox analysis and Lasso screening. Pathologic grading (p < 0.001) and model risk scores (p < 0.001) were found to be independent predictors of hepatocellular carcinoma (HCC) patient survival. Western blotting revealed higher levels of eosinophil peroxidase (EPX) in HCC tissues compared to adjacent normal tissues. Immunohistochemistry showed that eosinophils mainly infiltrated the connective tissue around HCC. The HCC samples showed low expression of RAMP3 and high expression of G6PD, SSRP1, and PLOD2, as detected by IHC and RT-qPCR analysis. The in vivo mouse experiments showed that IL-33 treatment induced the recruitment of eosinophils and reduced the number of intrahepatic tumor nodules. Conclusion: Overall, eosinophil infiltration in HCC is significantly correlated with patient survival. The risk assessment model based on eosinophil-related genes serves as a reliable clinical prognostic indicator and provides insights for precise treatment of HCC.

A stemness-based signature with inspiring indications in discriminating the prognosis, immune response, and somatic mutation of endometrial cancer patients revealed by machine learning

Endometrial cancer (EC) is a fatal gynecologic tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. Our study aimed to identify stemness-related prognostic biomarkers for new therapeutic strategies in EC. In this study, we explored the prognostic value of cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, and its correlation with immune infiltrates in EC. Transcriptome and somatic mutation profiles of EC were downloaded from TCGA database. Based on their stemness signature and DEGs, EC patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS and DFS than Stemness Subtype II. Subtype I also displayed better clinicopathological features, and genomic variations demonstrated different somatic mutation from subtype II. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. In the end, three machine learning algorithms were applied to construct a 7-gene stemness subtype risk model, which were further validated in an external independent EC cohort in our hospital. This novel stemness-based classification could provide a promising prognostic predictor for EC and may guide physicians in selecting potential responders for preferential use of immunotherapy. This novel stemness-dependent classification method has high value in predicting the prognosis, and also provides a reference for clinicians in selecting sensitive immunotherapy methods for EC patients.

Multiomics integration reveals NETosis heterogeneity and TLR2 as a prognostic biomarker in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant neoplasm characterized by a poor prognosis and limited therapeutic strategy. The PDAC tumor microenvironment presents a complex heterogeneity, where neutrophils emerge as the predominant constituents of the innate immune cell population. Leveraging the power of single-cell RNA-seq, spatial RNA-seq, and multi-omics approaches, we included both published datasets and our in-house patient cohorts, elucidating the inherent heterogeneity in the formation of neutrophil extracellular traps (NETs) and revealed the correlation between NETs and immune suppression. Meanwhile, we constructed a multi-omics prognostic model that suggested the patients exhibiting downregulated expression of NETs may have an unfavorable outcome. We also confirmed TLR2 as a potent prognosis factor and patients with low TLR2 expression had more effective T cells and an overall survival extension for 6 months. Targeting TLR2 might be a promising strategy to reverse immunosuppression and control tumor progression for an improved prognosis.

Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma

Epigenetic dysregulation drives aberrant transcriptional programs playing a critical role in hepatocellular carcinoma (HCC), which may provide novel insights into the heterogeneity of HCC. This study performed an integrated exploration on the epigenetic dysregulation of miRNA and methylation. We discovered and validated three patterns endowed with gene-related transcriptional traits and clinical outcomes. Specially, a stemness/epithelial-mesenchymal transition (EMT) subtype was featured by immune exhaustion and the worst prognosis. Besides, MMP12, a characteristic gene, was highly expressed in the stemness/EMT subtype, which was verified as a pivotal regulator linked to the unfavorable prognosis and further proven to promote tumor proliferation, invasion, and metastasis in vitro experiments. Proteomic analysis by mass spectrometry sequencing also indicated that the overexpression of MMP12 was significantly associated with cell proliferation and adhesion. Taken together, this study unveils innovative insights into epigenetic dysregulation and identifies a stemness/EMT subtype-specific gene, MMP12, correlated with the progression and prognosis of HCC.

Integrated clinical and prognostic analyses of mTOR/Hippo pathway core genes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most aggressive and dismal cancers globally. Emerging evidence has established that mTOR and Hippo pathways are oncogenic drivers of HCC. However, the prognostic value of these pathways in HCC remains unclear. In this study, we aimed to develop a gene signature utilizing the mTOR/Hippo genes for HCC prognostication. A multiple stage strategy was employed to screen, and a 12-gene signature based on mTOR/Hippo pathways was constructed to predict the prognosis of HCC patients. The risk scores calculated by the signature were inversely correlated with patient prognosis. Validation of the signature in independent cohort confirmed its predictive power. Further analysis revealed molecular differences between high and low-risk groups at genomic, transcriptomic, and protein-interactive levels. Moreover, immune infiltration analysis revealed an immunosuppressive state in the high-risk group. Finally, the gene signature could predict the sensitivity to current chemotherapeutic drugs. This study demonstrated that combinatorial mTOR/Hippo gene signature was a robust and independent prognostic tool for survival prediction of HCC. Our findings not only provide novel insights for the molecular understandings of mTOR/Hippo pathways in HCC, but also have important clinical implications for guiding therapeutic strategies.

Prognostic and metabolic characteristics of a novel cuproptosis-related signature in patients with hepatocellular carcinoma

Cuproptosis is a novel discovered mode of programmed cell death. To identify the molecular regulatory patterns related to cuproptosis, this study was designed for exploring the correlation between cuproptosis-related genes (CRGs) and the prognosis, metabolism, and treatment of hepatocellular carcinoma (HCC). Cancer Genome Atlas (TCGA) database was used to screen 363 HCC samples, which were categorized into 2 clusters based on the expression of CRGs. Survival analysis demonstrated that overall survival (OS) was better in Cluster 1 than Cluster 2 which might to be relevant to differences in metabolic based on functional analysis. With LASSO regression analysis and univariate COX regression, 8 prognosis-related genes were screened, a differently expressed genes (DEGs) were then constructed (HCC patients' DEGs)-based signature. The signature's stability was also validated in the 2 independent cohorts and test cohorts (GSE14520, HCC dataset in PCAWG). The 1-year, 3-year, and 5-year area under the curve (AUC) were 0.756, 0.706, and 0.722, respectively. The signature could also well predict the response to chemotherapy, targeted and transcatheter arterial chemoembolization (TACE) by providing a risk score. Moreover, the correlation was uncovered by the research between the metabolism and risk score. In conclusion, a unique cuproptosis-related signature that be capable of predicting patients' prognosis with HCC, and offered valuable insights into chemotherapy, TACE and targeted therapies for these patients has been developed.

Copper metabolism-related risk score identifies hepatocellular carcinoma subtypes and SLC27A5 as a potential regulator of cuproptosis

AIMS

Dysregulated copper metabolism has been noticed in many types of cancer including hepatocellular carcinoma (HCC); however, a comprehensive understanding about this dysregulation still remains unclear in HCC.

METHODS

A set of bioinformatic tools was integrated to analyze the expression and prognostic significance of copper metabolism-related genes. A related risk score, termed as CMscore, was developed via univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression. Pathway enrichment analyses and tumor immune cell infiltration were further investigated in CMscore stratified HCC patients. Weighted correlation network analysis (WGCNA) was used to identify potential regulator of cuproptosis.

RESULTS

Copper metabolism was dysregulated in HCC. HCC patients in the high-CMscore group showed a significantly lower overall survival (OS) and enriched in most cancer-related pathways. Besides, HCC patients with high CMscore had higher expression of pro-tumor immune infiltrates and immune checkpoints. Moreover, cancer patients with high CMscore from two large cohorts exhibited significantly prolonged survival time after immunotherapy. WGCNA and subsequently correlation analysis revealed that SLC27A5 might be a potential regulator of cuproptosis in HCC. In vitro experiments revealed that SLC27A5 inhibited cell proliferation and migration of HCC cells and could upregulate FDX1, the key regulator of cuproptosis.

SIGNIFICANCE

The CMscore is helpful in clustering HCC patients with distinct prognosis, gene mutation signatures, and sensitivity to immunotherapy. SLC27A5 might serve as a potential target in the induction of cuproptosis in HCC.

Pan-cancer landscape of immunology PIWI-interacting RNAs

PIWI-interacting RNAs (piRNAs), an emergent type of non-coding RNAs during oncogenesis, play critical roles in regulating tumor microenvironment. Systematic analysis of piRNAs' roles in modulating immune pathways is important for tumor immunotherapy. In this study, in-depth analysis of piRNAs was performed to develop an integrated computational algorithm, the immunology piRNA (ImmPI) pipeline, for uncovering the global expression landscape of piRNAs and identifying their regulatory roles in immune pathways. The immunology piRNAs show a tendency towards overexpression patterns in immune cells, causing perturbations in tumors, being significantly associated with infiltration of immune cells, and having prognostic value. The ImmPI score can contribute to prioritizing tumor-related piRNAs and distinguish two subtypes of SKCM (immune-cold and hot phenotypes), as characterized by different prognoses, immunogenicity and antitumor immunity. Finally, we developed an interactive web resource (ImmPI portal: http://www.hbpding.com/ImmPi) for the biomedical research community, with several useful modules to browse, visualize, and download the results of immunology piRNAs analysis. Overall, our work provides a comprehensive landscape of piRNAs across multiple cancer types and sheds light on their regulatory and functional roles in tumor immunity. These findings pave the way for future research and development of piRNA-based immunotherapies for cancer treatment.

Diverse genome-wide DNA methylation alterations in canine hepatocellular tumours

BACKGROUND

Canine hepatocellular tumours (HCTs) are common primary liver tumours. However, the exact mechanisms of tumourigenesis remain unclear. Although some genetic mutations have been reported, DNA methylation alterations in canine HCT have not been well studied.

OBJECTIVES

In this study, we aimed to analyse the DNA methylation status of canine HCT.

METHODS

Tissues from 33 hepatocellular carcinomas, 3 hepatocellular adenomas, 1 nodular hyperplasia, 21 non-tumour livers from the patients and normal livers from 5 healthy dogs were used. We analysed the DNA methylation levels of 72,367 cytosine-guanine dinucleotides (CpG sites) in all 63 samples.

RESULTS AND CONCLUSIONS

Although a large fraction of CpG sites that were highly methylated in the normal liver became hypomethylated in tumours from most patients, we also found some patients with less remarkable change or no change in DNA methylation. Hierarchical clustering analysis revealed that 32 of 37 tumour samples differed from normal livers, although the remaining 5 tumour livers fell into the same cluster as normal livers. In addition, the number of hypermethylated genes in tumour livers varied among tumour cases, suggesting various DNA methylation patterns in different tumour groups. However, patient and clinical parameters, such as age, were not associated with DNA methylation status. In conclusion, we found that HCTs undergo aberrant and diverse patterns of genome-wide DNA methylation compared with normal liver tissue, suggesting a complex epigenetic mechanism in canine HCT.

IMOPAC: A web server for interactive multiomics and pharmacological analyses of patient-derived cancer cell lines

Large-scale multidimensional cancer genomic and pharmacological profiles have been created by several large consortium projects, including NCI-60, GDSC and DepMap, providing novel opportunities for data mining and further understanding of intrinsic therapeutic response mechanisms. However, it is increasingly challenging for experimental biologists, especially those without a bioinformatic background, to integrate, explore, and analyse these tremendous pharmacogenomics. To address this gap, IMOPAC, an interactive and easy-to-use web-based tool, was introduced to provide rapid visualizations and customizable functionalities on the basis of these three publicly available databases, which may reduce pharmacogenomic profiles from cell lines into readily understandable genetic, epigenetic, transcriptionomic, proteomic, metabolomic, and pharmacological events. The user-friendly query interface together with customized data storage enables users to interactively investigate and visualize multiomics alterations across genes and pathways and to link these alterations with drug responses across cell lines from diverse cancer types. The analyses in our portal include pancancer expression, drug-omics/pathway correlation, cancer subtypes, omics-omics (cis-/trans-regulation) correlation, fusion query analysis, and drug response prediction analysis. The comprehensive multiomics and pharmacogenomic analyses with simple clicking through IMOPAC will significantly benefit cancer precision medicine, contribute to the discoveries of potential biological mechanisms and facilitate pharmacogenomics mining in the identification of clinically actionable biomarkers for both basic researchers and clinical practitioners. IMOPAC is freely available at http://www.hbpding.com/IMOPAC.

The prognostic value and immune landscaps of m6A/m5C-related lncRNAs signature in the low grade glioma

BACKGROUND

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) are the main RNA methylation modifications involved in the oncogenesis of cancer. However, it remains obscure whether m6A/m5C-related long non-coding RNAs (lncRNAs) affect the development and progression of low grade gliomas (LGG).

METHODS

We summarized 926 LGG tumor samples with RNA-seq data and clinical information from The Cancer Genome Atlas and Chinese Glioma Genome Atlas. 105 normal brain samples with RNA-seq data from the Genotype Tissue Expression project were collected for control. We obtained a molecular classification cluster from the expression pattern of sreened lncRNAs. The least absolute shrinkage and selection operator Cox regression was employed to construct a m6A/m5C-related lncRNAs prognostic signature of LGG. In vitro experiments were employed to validate the biological functions of lncRNAs in our risk model.

RESULTS

The expression pattern of 14 sreened highly correlated lncRNAs could cluster samples into two groups, in which various clinicopathological features and the tumor immune microenvironment were significantly distinct. The survival time of cluster 1 was significantly reduced compared with cluster 2. This prognostic signature is based on 8 m6A/m5C-related lncRNAs (GDNF-AS1, HOXA-AS3, LINC00346, LINC00664, LINC00665, MIR155HG, NEAT1, RHPN1-AS1). Patients in the high-risk group harbored shorter survival times. Immunity microenvironment analysis showed B cells, CD4 + T cells, macrophages, and myeloid-derived DC cells were significantly increased in the high-risk group. Patients in high-risk group had the worse overall survival time regardless of followed TMZ therapy or radiotherapy. All observed results from the TCGA-LGG cohort could be validated in CGGA cohort. Afterwards, LINC00664 was found to promote cell viability, invasion and migration ability of glioma cells in vitro.

CONCLUSION

Our study elucidated a prognostic prediction model of LGG by 8 m6A/m5C methylated lncRNAs and a critical lncRNA regulation function involved in LGG progression. High-risk patients have shorter survival times and a pro-tumor immune microenvironment.

Recent advances in understanding DNA methylation of prostate cancer

Epigenetic modifications, such as DNA methylation, is widely studied in cancer. DNA methylation patterns have been shown to distinguish between benign and malignant tumors in various cancers, including prostate cancer. It may also contribute to oncogenesis, as it is frequently associated with downregulation of tumor suppressor genes. Aberrant patterns of DNA methylation, in particular the CpG island hypermethylator phenotype (CIMP), have shown associative evidence with distinct clinical features and outcomes, such as aggressive subtypes, higher Gleason score, prostate-specific antigen (PSA), and overall tumor stage, overall worse prognosis, as well as reduced survival. In prostate cancer, hypermethylation of specific genes is significantly different between tumor and normal tissues. Methylation patterns could distinguish between aggressive subtypes of prostate cancer, including neuroendocrine prostate cancer (NEPC) and castration resistant prostate adenocarcinoma. Further, DNA methylation is detectable in cell-free DNA (cfDNA) and is reflective of clinical outcome, making it a potential biomarker for prostate cancer. This review summarizes recent advances in understanding DNA methylation alterations in cancers with the focus on prostate cancer. We discuss the advanced methodology used for evaluating DNA methylation changes and the molecular regulators behind these changes. We also explore the clinical potential of DNA methylation as prostate cancer biomarkers and its potential for developing targeted treatment of CIMP subtype of prostate cancer.

A Comprehensive Analysis of Programmed Cell Death-Associated Genes for Tumor Microenvironment Evaluation Promotes Precise Immunotherapy in Patients with Lung Adenocarcinoma

Immune checkpoint inhibitors (ICIs) represent a new hot spot in tumor therapy. Programmed cell death has an important role in the prognosis. We explore a programmed cell death gene prognostic model associated with survival and immunotherapy prediction via computational algorithms. Patient details were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. We used LASSO algorithm and multiple-cox regression to establish a programmed cell death-associated gene prognostic model. Further, we explored whether this model could evaluate the sensitivity of patients to anti-PD-1/PD-L1. In total, 1342 patients were included. We constructed a programmed cell death model in TCGA cohorts, and the overall survival (OS) was significantly different between the high- and low-risk score groups (HR 2.70; 95% CI 1.94-3.75; p < 0.0001; 3-year OS AUC 0.71). Specifically, this model was associated with immunotherapy progression-free survival benefit in the validation cohort (HR 2.42; 95% CI 1.59-3.68; p = 0.015; 12-month AUC 0.87). We suggest that the programmed cell death model could provide guidance for immunotherapy in LUAD patients.

Epigenetics in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and has a high fatality rate. Genetic and epigenetic aberrations are commonly observed in HCC. The epigenetic processes include chromatin remodelling, histone alterations, DNA methylation, and noncoding RNA (ncRNA) expression and are connected with the progression and metastasis of HCC. Due to their potential reversibility, these epigenetic alterations are widely targeted for the development of biomarkers. In-depth understanding of the epigenetics of HCC is critical for developing rational clinical strategies that can provide a meaningful improvement in overall survival and prediction of therapeutic outcomes. In this article, we have summarised the epigenetic modifications involved in HCC progression and highlighted the potential biomarkers for diagnosis and drug development.

Clinical and Prognostic Value of PPIA, SQSTM1, and CCL20 in Hepatocellular Carcinoma Patients by Single-Cell Transcriptome Analysis

Hepatocellular carcinoma (HCC) is the most malignant and poor-prognosis subtype of primary liver cancer. The scRNA-seq approach provides unique insight into tumor cell behavior at the single-cell level. Cytokine signaling in the immune system plays an important role in tumorigenesis and has both pro-tumorigenic and anti-tumorigenic functions. A biomarker of cytokine signaling in immune-related genes (CSIRG) is urgently required to assess HCC patient diagnosis and treatment. By analyzing the expression profiles of HCC single cells, TCGA, and ICGC data, we discovered that three important CSIRG (PPIA, SQSTM1, and CCL20) were linked to the overall survival of HCC patients. Cancer status and three hub CSIRG were taken into account while creating a risk nomogram. The nomogram had a high level of predictability and accuracy. Based on the CSIRG risk score, a distinct pattern of somatic tumor mutational burden (TMB) was detected between the two groups. The enrichment of the pyrimidine metabolism pathway, purine metabolism pathway, and lysosome pathway in HCC was linked to the CSIRG high-risk scores. Overall, scRNA-seq and bulk RNA-seq were used to create a strong CSIRG signature for HCC diagnosis.

CIMP-positive glioma is associated with better prognosis: A systematic analysis

BACKGROUND

CpG island methylator phenotype (CIMP) was closely related to the degree of pathological differentiation of tumors, and it's an important determinant of glioma pathogenicity. However, the molecular and pathological features of CIMP-positive glioma have not been fully elucidated. In addition, CIMP have been reported to be a useful prognostic marker in several human cancers, yet its prognostic value in gliomas is still controversial. Therefore, we aimed to evaluate gene mutations and pathological features of CIMP-positive glioma and explore the prognostic value of CIMP in gliomas.

METHODS

We comprehensively searched PubMed, Embase, and MEDLINE for studies describing gene mutations, pathological features and overall survival of gliomas stratified by CIMP status. Odds ratios (OR), hazard ratios (HR), and their 95% confidence intervals (CI) were used to estimate the correlation between CIMP and the outcome parameters.

RESULTS

Twelve studies with 2386 gliomas (1051 CIMP-positive and 1335 CIMP-negative) were included. Our results showed that CIMP was more frequent in isocitrate dehydrogenase 1 (IDH1)-mutated gliomas (OR 229.07; 95% CI 138.72-378.26) and 1p19q loss of heterozygosis (LOH) gliomas (OR 5.65; 95% CI 2.66-12.01). Pathological analysis showed that CIMP was common in low-malignant oligodendroglioma (OR 5.51; 95% CI 3.95-7.70) with molecular features including IDH1 mutations and 1p19q LOH, but rare in glioblastoma (OR 0.14; 95% CI 0.10-0.19). However, CIMP showed no obvious correlation with anaplastic oligoastrocytomas (OR 1.57; 95% CI 1.24-2.00) or oligoastrocytomas (OR 0.79; 95% CI 0.35-1.76). Concerning the prognosis, we found that CIMP-positive gliomas had longer overall survival (HR 0.57; 95% CI 0.97-0.16) than CIMP-negative gliomas.

CONCLUSIONS

CIMP could be used as a potential independent prognostic indicator for glioma.

M2-like tumor-associated macrophage-related biomarkers to construct a novel prognostic signature, reveal the immune landscape, and screen drugs in hepatocellular carcinoma

Background

M2-like tumor-associated macrophages (M2-like TAMs) have important roles in the progression and therapeutics of cancers. We aimed to detect novel M2-like TAM-related biomarkers in hepatocellular carcinoma (HCC) via integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to construct a novel prognostic signature, reveal the “immune landscape”, and screen drugs in HCC.

Methods

M2-like TAM-related genes were obtained by overlapping the marker genes of TAM identified from scRNA-seq data and M2 macrophage modular genes identified by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were carried out to screen prognostic genes from M2-like TAM-related genes, followed by a construction of a prognostic signature, delineation of risk groups, and external validation of the prognostic signature. Analyses of immune cells, immune function, immune evasion scores, and immune-checkpoint genes between high- and low-risk groups were done to further reveal the immune landscape of HCC patients. To screen potential HCC therapeutic agents, analyses of gene–drug correlation and sensitivity to anti-cancer drugs were conducted.

Results

A total of 127 M2-like TAM-related genes were identified by integrative analysis of scRNA-seq and bulk-seq data. PDLIM3, PAM, PDLIM7, FSCN1, DPYSL2, ARID5B, LGALS3, and KLF2 were screened as prognostic genes in HCC by univariate Cox regression and LASSO regression analyses. Then, a prognostic signature was constructed and validated based on those genes for predicting the survival of HCC patients. In terms of drug screening, expression of PAM and LGALS3 was correlated positively with sensitivity to simvastatin and ARRY-162, respectively. Based on risk grouping, we predicted 10 anticancer drugs with high sensitivity in the high-risk group, with epothilone B having the lowest half-maximal inhibitory concentration among all drugs tested.

Conclusions

Our findings enhance understanding of the M2-like TAM-related molecular mechanisms involved in HCC, reveal the immune landscape of HCC, and provide potential targets for HCC treatment.

A miR-9-5p/FOXO1/CPEB3 Feed-Forward Loop Drives the Progression of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide, but its regulatory mechanism remains unclear and potential clinical biomarkers are still lacking. Co-regulation of TFs and miRNAs in HCC and FFL module studies may help to identify more precise and critical driver modules in HCC development. Here, we performed a comprehensive gene expression and regulation analysis for HCC in vitro and in vivo. Transcription factor and miRNA co-regulatory networks for differentially expressed genes between tumors and adjacent tissues revealed the critical feed-forward loop (FFL) regulatory module miR-9-5p/FOXO1/CPEB3 in HCC. Gain- and loss-of-function studies demonstrated that miR-9-5p promotes HCC tumor proliferation, while FOXO1 and CPEB3 inhibit hepatocarcinoma growth. Furthermore, by luciferase reporter assay and ChIP-Seq data, CPEB3 was for the first time identified as a direct downstream target of FOXO1, negatively regulated by miR-9-5p. The miR-9-5p/FOXO1/CPEB3 FFL was associated with poor prognosis, and promoted cell growth and tumor progression of HCC in vitro and in vivo. Our study identified for the first time the existence of miR-9-5p/FOXO1/CPEB3 FFL and revealed its regulatory role in HCC progression, which may represent a new potential target for cancer therapy.

Identification of a seven-gene signature predicting clinical outcome of liver cancer based on tumor mutational burden

The total number of somatic mutations may affect the prognosis of cancer, so we applied bioinformatics methods to investigate the association between the TMB (tumor mutational burden)-related differentially expressed genes (DEGs) and the prognosis of hepatocellular carcinoma (HCC). We calculated the TMB value of the patients with HCC in TCGA database and identified the differentially expressed genes between the high-TMB and low-TMB patients. We performed functional enrichment analysis and LASSO Cox regression analysis of the DEGs, and seven genes were screened to establish a risk score model. A nomogram based on the risk scores was drawn to assess the predictive outcomes compared to the actual outcomes. The expression level of the seven genes was verified in cancer cell lines. Moreover, we explored the difference in immune cells infiltration and immune checkpoints between the high-risk and low-risk groups. The results showed that the DEGs between the high-TMB and low-TMB patients were enriched in extracellular matrix organization. A seven-gene risk score model (PAGE1, CHGA, OGN, MMP7, TRIM55, MAGEA6, and MAGEA12) was established for predicting HCC prognosis. Patients with lower risk scores had longer survival time and lower mortality rate. The nomogram based on risk scores and TNM staging showed good performance and reliability in predicting the clinical outcomes. Significant differences in cell infiltration and checkpoints were found between the high-risk and low-risk groups. Our study demonstrated a seven-gene signature and a nomogram based on the risk score model to predict the prognosis of HCC. Some of the newly identified DEGs may be potential biomarkers or therapeutic targets.

Machine learning and bioinformatics analysis revealed classification and potential treatment strategy in stage 3-4 NSCLC patients

BACKGROUND

Precision medicine has increased the accuracy of cancer diagnosis and treatment, especially in the era of cancer immunotherapy. Despite recent advances in cancer immunotherapy, the overall survival rate of advanced NSCLC patients remains low. A better classification in advanced NSCLC is important for developing more effective treatments.

METHOD

The calculation of abundances of tumor-infiltrating immune cells (TIICs) was conducted using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), xCell (xCELL), Tumor IMmune Estimation Resource (TIMER), Estimate the Proportion of Immune and Cancer cells (EPIC), and Microenvironment Cell Populations-counter (MCP-counter). K-means clustering was used to classify patients, and four machine learning methods (SVM, Randomforest, Adaboost, Xgboost) were used to build the classifiers. Multi-omics datasets (including transcriptomics, DNA methylation, copy number alterations, miRNA profile) and ICI immunotherapy treatment cohorts were obtained from various databases. The drug sensitivity data were derived from PRISM and CTRP databases.

RESULTS

In this study, patients with stage 3-4 NSCLC were divided into three clusters according to the abundance of TIICs, and we established classifiers to distinguish these clusters based on different machine learning algorithms (including SVM, RF, Xgboost, and Adaboost). Patients in cluster-2 were found to have a survival advantage and might have a favorable response to immunotherapy. We then constructed an immune-related Poor Prognosis Signature which could successfully predict the advanced NSCLC patient survival, and through epigenetic analysis, we found 3 key molecules (HSPA8, CREB1, RAP1A) which might serve as potential therapeutic targets in cluster-1. In the end, after screening of drug sensitivity data derived from CTRP and PRISM databases, we identified several compounds which might serve as medication for different clusters.

CONCLUSIONS

Our study has not only depicted the landscape of different clusters of stage 3-4 NSCLC but presented a treatment strategy for patients with advanced NSCLC.

Development of a prognostic gene signature for hepatocellular carcinoma

Accurate prediction of overall survival is important for prognosis and the assignment of appropriate personalized clinical treatment in hepatocellular carcinoma (HCC) patients. The aim of the present study was to establish an optimal gene model for the independent prediction of prognosis associated with common clinical patterns. Gene expression profiles and the corresponding clinical information of the LIHC cohort were obtained from The Cancer Genome Atlas. Differentially expressed genes were found using the R package "limma". Subsequently, a prognostic gene signature was developed using the LASSO Cox regression model. Kaplan-Meier, log-rank, and receiver operating characteristic (ROC) analyses were performed to verify the predictive accuracy of the prognostic model. Finally, a nomogram and calibration plot were created using the "rms" package. Differentially expressed genes were screened with threshold criteria (FDR < 0.01 and |log FC|>3) and 563 differentially expressed genes were obtained, including 448 downregulated and 115 upregulated genes. Using the LASSO Cox regression model, a prognostic gene signature was developed based on nine genes, IQGAP3, BIRC5, PTTG1, STC2, CDKN3, PBK, EXO1, NEIL3, and HOXD9, the expression levels of which were quantitated using RT-qPCR. According to the risk scores, patients were separated into high-risk and low-risk groups. In conclusion, the prognostic gene signature can be used as a combined biomarker for the independent prediction of overall survival in HCC patients. Moreover, we created a nomogram that can be used to infer prognosis and aid individualized decisions regarding treatment and surveillance.

Establishment of A Nomogram for Predicting the Prognosis of Soft Tissue Sarcoma Based on Seven Glycolysis-Related Gene Risk Score

Background: Soft tissue sarcoma (STS) is a group of tumors with a low incidence and a complex type. Therefore, it is an arduous task to accurately diagnose and treat them. Glycolysis-related genes are closely related to tumor progression and metastasis. Hence, our study is dedicated to the development of risk characteristics and nomograms based on glycolysis-related genes to assess the survival possibility of patients with STS.

Methods: All data sets used in our research include gene expression data and clinical medical characteristics in the Genomic Data Commons Data Portal (National Cancer Institute) Soft Tissue Sarcoma (TCGA SARC) and GEO database, gene sequence data of corresponding non-diseased human tissues in the Genotype Tissue Expression (GTEx).Next, transcriptome data in TCGA SARC was analyzed as the training set to construct a glycolysis-related gene risk signature and nomogram, which were confirmed in external test set.

Results: We identified and verified the 7 glycolysis-related gene signature that is highly correlated with the overall survival (OS) of STS patients, which performed excellently in the evaluation of the size of AUC, and calibration curve. As well as, the results of the analysis of univariate and multivariate Cox regression demonstrated that this 7 glycolysis-related gene characteristic acts independently as an influence predictor for STS patients. Therefore, a prognostic-related nomogram combing 7 gene signature with clinical influencing features was constructed to predict OS of patients with STS in the training set that demonstrated strong predictive values for survival.

Conclusion: These results demonstrate that both glycolysis-related gene risk signature and nomogram were efficient prognostic indicators for patients with STS. These findings may contribute to make individualize clinical decisions on prognosis and treatment.

GRAMD4 inhibits tumour metastasis by recruiting the E3 ligase ITCH to target TAK1 for degradation in hepatocellular carcinoma

BACKGROUND

Aberrant TAK1 (transforming growth factor β-activated kinase 1) activity is known to be involved in a variety of malignancies, but the regulatory mechanisms of TAK1 remain poorly understood. GRAMD4 (glucosyltransferase Rab-like GTPase activator and myotubularin domain containing 4) is a newly discovered p53-independent proapoptotic protein with an unclear role in HCC (hepatocellular carcinoma).

RESULTS

In this research, we found that GRAMD4 expression was lower in HCC samples, and its downregulation predicted worse prognosis for patients after surgical resection. Functionally, GRAMD4 inhibited HCC migration, invasion and metastasis. Mechanistically, GRAMD4 interacted with TAK1 to promote its protein degradation, thus, resulting in the inactivation of MAPK (Mitogen-activated protein kinase) and NF-κB pathways. Furthermore, GRAMD4 was proved to recruit ITCH (itchy E3 ubiquitin protein ligase) to promote the ubiquitination of TAK1. Moreover, high expression of TAK1 was correlated with low expression of GRAMD4 in HCC patients.

CONCLUSIONS

GRAMD4 inhibits the migration and metastasis of HCC, mainly by recruiting ITCH to promote the degradation of TAK1, which leads to the inactivation of MAPK and NF-κB signalling pathways.

Integrated bioinformatics analysis of the NEDD4 family reveals a prognostic value of NEDD4L in clear-cell renal cell cancer

The members of the Nedd4-like E3 family participate in various biological processes. However, their role in clear cell renal cell carcinoma (ccRCC) is not clear. This study systematically analyzed the Nedd4-like E3 family members in ccRCC data sets from multiple publicly available databases. NEDD4L was identified as the only NEDD4 family member differentially expressed in ccRCC compared with normal samples. Bioinformatics tools were used to characterize the function of NEDD4L in ccRCC. It indicated that NEDD4L might regulate cellular energy metabolism by co-expression analysis, and subsequent gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A prognostic model developed by the LASSO Cox regression method showed a relatively good predictive value in training and testing data sets. The result revealed that NEDD4L was associated with biosynthesis and metabolism of ccRCC. Since NEDD4L is downregulated and dysregulation of metabolism is involved in tumor progression, NEDD4L might be a potential therapeutic target in ccRCC.

Pan-cancer analysis of the prognostic value of C12orf75 based on data mining

The differential expression of chromosome 12 open reading frame 75 (C12orf75) is closely related with cancer progression. Here, we studied the expression levels of C12orf75 and investigated its prognostic value in various cancers across distinct datasets including ONCOMINE, PrognoScan, GEPIA, and TCGA. The correlation between genetic alteration of C12orf75 and immune infiltration was investigated using the cBioPortal and TIMER databases. RNA interference was used to verify the influence of C12orf75 knockdown on the biological phenotype of hepatocellular carcinoma cells. C12orf75 showed increased expression in most tested human cancers. The increased expression of C12orf75 was related with a poor prognosis in urothelial bladder carcinoma and hepatocellular liver carcinoma, but it was surprisingly converse in renal papillary cell carcinoma. In urothelial bladder carcinoma and hepatocellular liver carcinoma, we observed positive correlations between the expression of C12orf75 and the infiltration of immune cells, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. The knockdown of C12orf75 in hepatocellular carcinoma cells suppressed the proliferation, migration, and invasion and arrested the cell cycle. This is the first report C12orf75 has potential as a prognostic biomarker and therapeutic target for molecularly targeted drugs in urothelial bladder carcinoma, hepatocellular liver carcinoma, and renal papillary cell carcinoma.

Prognostic value of members of NFAT family for pan-cancer and a prediction model based on NFAT2 in bladder cancer

Bladder cancer (BLCA) is one of the common malignant tumors of the urinary system. The poor prognosis of BLCA patients is due to the lack of early diagnosis and disease recurrence after treatment. Increasing evidence suggests that gene products of the nuclear factor of activated T-cells (NFAT) family are involved in BLCA progression and subsequent interaction(s) with immune surveillance. In this study, we carried out a pan-cancer analysis of the NFAT family and found that NFAT2 is an independent prognostic factor for BLCA. We then screened for differentially expressed genes (DEGs) and further analyzed such candidate gene loci using gene ontology enrichment to curate the KEGG database. We then used Lasso and multivariate Cox regression to identify 4 gene loci (FER1L4, RNF128, EPHB6, and FN1) which were screened together with NFAT2 to construct a prognostic model based on using Kaplan-Meier analysis to predict the overall survival of BLCA patients. Moreover, the accuracy of our proposed model is supported by deposited datasets in the Gene Expression Omnibus (GEO) database. Finally, a nomogram of this prognosis model for BLCA was established which could help to provide better disease management and treatment.

Integrative analysis identifies key mRNA biomarkers for diagnosis, prognosis, and therapeutic targets of HCV-associated hepatocellular carcinoma

Hepatitis C virus-associated HCC (HCV-HCC) is a prevalent malignancy worldwide and the molecular mechanisms are still elusive. Here, we screened 240 differentially expressed genes (DEGs) of HCV-HCC from Gene expression omnibus (GEO) and the Cancer Genome Atlas (TCGA), followed by weighted gene coexpression network analysis (WGCNA) to identify the most significant module correlated with the overall survival. 10 hub genes (CCNB1, AURKA, TOP2A, NEK2, CENPF, NUF2, CDKN3, PRC1, ASPM, RACGAP1) were identified by four approaches (Protein-protein interaction networks of the DEGs and of the significant module by WGCNA, and diagnostic and prognostic values), and their abnormal expressions, diagnostic values, and prognostic values were successfully verified. A four hub gene-based prognostic signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm and a multivariate Cox regression model with the ICGC-LIRI-JP cohort (N =112). Kaplan-Meier survival plots (P = 0.0003) and Receiver Operating Characteristic curves (ROC = 0.778) demonstrated the excellent predictive potential for the prognosis of HCV-HCC. Additionally, upstream regulators including transcription factors and miRNAs of hub genes were predicted, and candidate drugs or herbs were identified. These findings provide a firm basis for the exploration of the molecular mechanism and further clinical biomarkers development of HCV-HCC.

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection. Considering that GBM cancer stem cells (CSCs) play a non-negligible role in tumorigenesis and chemoradiotherapy resistance, we proposed a novel stemness-based classification of GBM and screened out certain population more responsive to immunotherapy. The one-class logistic regression algorithm was used to calculate the stemness index (mRNAsi) of 518 GBM patients from The Cancer Genome Atlas (TCGA) database based on transcriptomics of GBM and pluripotent stem cells. Based on their stemness signature, GBM patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS but poorer progression-free survival than Stemness Subtype II. Genomic variations revealed patients in Stemness Subtype I had higher somatic mutation loads and copy number alteration burdens. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. Tumor Immune Dysfunction and Exclusion and subclass mapping analysis further demonstrated patients in Stemness Subtype I were more likely to respond to immunotherapy, especially anti-PD1 treatment. The pRRophetic algorithm also indicated patients in Stemness Subtype I were more resistant to temozolomide therapy. Finally, multiple machine learning algorithms were used to develop a 7-gene Stemness Subtype Predictor, which were further validated in two external independent GBM cohorts. This novel stemness-based classification could provide a promising prognostic predictor for GBM and may guide physicians in selecting potential responders for preferential use of immunotherapy.

Overexpression of Taspase 1 Predicts Poor Prognosis in Patients with Hepatocellular Carcinoma

Background

Taspase 1 (TASP1) is a highly conserved protease involved in site-specific proteolysis. Existing researches have revealed a link between TASP1 expression and carcinogenesis. However, limited data are available regarding the prognosis and functions of TASP1 in hepatocellular carcinoma (HCC).

Methods

Western Blotting and qRT-PCR were employed to evaluate the level of TASP1 in HCC cell lines and clinical specimens. TASP1 expression was further calculated in clinical specimens by immunohistochemistry and the mRNA level of TASP1 in HCC was analyzed using Oncomine and UALCAN databases. The TASP1 promoter methylation modification was shown via MEXPRESS and UALCAN. The association between TASP1 expression and postoperative prognosis was evaluated using Kaplan–Meier and Cox regression analysis in clinical patients. The effect of TASP1 on HCC prognosis was analyzed via Kaplan-Meier plotter, GEPIA and UALCAN. Additionally, the regulators, kinases, miRNA and transcription factor targets of TASP1 were identified using LinkedOmics. Moreover, cBioPortal was used to detect the genetic alteration of TASP1. Finally, TIMER was utilized to assess the relation between TASP1 and the immune cell infiltration, whereas the correlation of TASP1 with three immune factors was detected through TISIDB.

Results

TASP1 expression was increased in HCC cell lines and HCC tissues. CNV and DNA methylation of TASP1 were changed. Survival analysis revealed that high TASP1 expression was correlated with overall survival (OS). Functional network analysis about TASP1 in HCC showed that the double-strand break repair, peptidyl-threonine modification, spindle organization, peptidyl-lysine modification and microtubule-based movement were modulated. Furthermore, TASP1 expression revealed puissant relation to the infiltration of immune cells and three immune factors in HCC.

Conclusion

These data indicate that TASP1 may act as a potential prognostic marker in HCC and regulate HCC via multiple mechanisms.

Identification of a Novel Epithelial-to-Mesenchymal-related Gene Signature in Predicting Survival of Patients with Hepatocellular Carcinoma

BACKGROUND

Epithelial-mesenchymal transformation (EMT) promotes cancer metastasis including hepatocellular carcinoma. Therefore, EMT-related gene signature was explored.

OBJECTIVE

The present study was designed to develop an EMT-related gene signature for predicting the prognosis of patients with hepatocellular carcinoma.

METHODS

We conducted an integrated gene expression analysis based on tumor data of the patients with hepatocellular carcinoma from The Cancer Genome Atlas (TCGA), HCCDB18 and GSE14520 dataset. An EMT-related gene signature was constructed by least absolute shrinkage and selection operator (LASSO) and COX regression analysis of univariate and multivariate survival.

RESULTS

A 3-EMT gene signature was developed and validated based on gene expression profiles of hepatocellular carcinoma from three microarray platforms. Patients with a high risk score had a significantly worse overall survival (OS) than those with low risk scores. The EMT-related gene signature showed a high performance in accurately predicting prognosis and in examining the clinical characteristics and immune score analysis. Univariate and multivariate Cox regression analyses confirmed that the EMT-related gene signature was an independent prognostic factor for predicting survival in hepatocellular carcinoma patients. Compared with the existing models, our EMT-related gene signature reached higher area under curve (AUC).

CONCLUSION

Our findings provide novel insight into understanding EMT and help identify hepatocellular carcinoma patients with poor prognosis.

A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice.

METHODS

Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model's effectiveness.

RESULTS

We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases.

CONCLUSION

Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

Development and validation of the trans-omics model for pancreatic adenocarcinoma

Aim: To develop a trans-omics-based molecular clinicopathological algorithm for predicting pancreatic adenocarcinoma prognosis, we performed a comprehensive analysis of the expression levels of mRNA, DNA methylation and DNA copy number in The Cancer Genome Atlas dataset. Materials & methods: Based on the least absolute shrinkage and selection operator method - COX regression analysis, a trans-omics-based classifier was established to predict overall survival. Nomogram was constructed by combining the classifier band clinical pathological characterization. Results: Based on trans-omics, we developed a 10-gene-based classifier and a molecular-clinicopathologic nomogram for predicting overall survival with satisfactory accuracy. Conclusion: Trans-omics-based classifier and molecule-clinicopathological nomogram based on the classifier can accurately predict the prognosis of pancreatic adenocarcinoma patients.

Development and validation of a CTNNB1-associated metabolic prognostic model for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy closely related to metabolic reprogramming. We investigated how CTNNB1 mutation regulates the HCC metabolic phenotype and thus affects the prognosis of HCC. We obtained the mRNA expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA), the International Cancer Genomics Consortium (ICGC) and the Gene Expression Omnibus database (GSE14520 and GSE116174). We conducted gene set enrichment analysis on HCC patients with and without mutant CTNNB1 through TCGA dataset. The Kaplan‐Meier analysis and univariate Cox regression analysis assisted in screening metabolic genes related to prognosis, and the prognosis model was constructed using the Lasso and multivariate Cox regression analysis. The prognostic model showed good prediction performance in both the training cohort (TCGA) and the validation cohorts (ICGC, GSE14520, GSE116174), and the high‐risk group presented obviously poorer overall survival compared with low‐risk group. Cox regression analysis indicated that the risk score can be used as an independent predictor for the overall survival of HCC. The immune infiltration in different risk groups was also evaluated in this study to explore underlying mechanisms. This study is also the first to describe an metabolic prognostic model associated with CTNNB1 mutations and could be implemented for determining the prognoses of individual patients in clinical practice.

Epigenetics in hepatocellular carcinoma development and therapy: The tip of the iceberg

Hepatocellular carcinoma (HCC) is a deadly tumour whose causative agents are generally well known, but whose pathogenesis remains poorly understood. Nevertheless, key genetic alterations are emerging from a heterogeneous molecular landscape, providing information on the tumorigenic process from initiation to progression. Among these molecular alterations, those that affect epigenetic processes are increasingly recognised as contributing to carcinogenesis from preneoplastic stages. The epigenetic machinery regulates gene expression through intertwined and partially characterised circuits involving chromatin remodelers, covalent DNA and histone modifications, and dedicated proteins reading these modifications. In this review, we summarise recent findings on HCC epigenetics, focusing mainly on changes in DNA and histone modifications and their carcinogenic implications. We also discuss the potential drugs that target epigenetic mechanisms for HCC treatment, either alone or in combination with current therapies, including immunotherapies.

Integrated TCGA and GEO analysis showed that SMAD7 is an independent prognostic factor for lung adenocarcinoma

The lack of effective markers leads to missed optimal treatment times, resulting in poorer prognosis in most cancers. Drosophila mothers against decapentaplegic protein (SMAD) family members are important cytokines in the transforming growth factor-beta family. They jointly regulate the processes of cell growth, differentiation, and apoptosis. However, the expression of SMAD family genes in pan-cancers and their impact on prognosis have not been elucidated. Perl software and R software were used to perform expression analysis and survival curve analysis on the data collected by TCGA, GTEx, and GEO, and the potential regulatory pathways were determined through gene ontology enrichment and kyoto encyclopedia of genes and genomes enrichment analysis. It was found that SMAD7 and SMAD9 expression decreased in lung adenocarcinoma (LUAD), and their expression was positively correlated with survival time. Additionally, SMAD7 could be used as an independent prognostic factor for LUAD. In general, SMAD7 and SMAD9 can be used as prognostic markers of LUAD. Further, SMAD7 is expected to become a therapeutic target for LUAD.

A Novel RNA Binding Protein-Related Prognostic Signature for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a highly malignant and aggressive cancer with high recurrence rates and mortality. Some studies have illustrated that RNA binding proteins (RBPs) were involved in the carcinogenesis and development of multiple cancers, but the roles in HCC were still unclear. We downloaded the RNA-seq and corresponding clinical information of HCC from The Cancer Genome Atlas (TCGA) database, and 330 differentially expressed RBPs were identified between normal and HCC tissues. Through series of the univariate, the least absolute shrinkage selection operator (LASSO), and the stepwise multivariate Cox regression analyses, six prognosis-related key RBPs (CNOT6, UPF3B, MRPL54, ZC3H13, IFIT5, and PPARGC1A) were screened out from DE RBPs, and a six-RBP gene risk score signature was constructed in training set. Survival analysis indicated that HCC patients with high-risk scores had significantly worse overall survival than low-risk patients, and furthermore, the signature can be used as an independent prognostic indicator. The good accuracy of this prognostic signature was confirmed by the ROC curve analysis and was further validated in the International Cancer Genome Consortium (ICGC) HCC cohort. Besides, a nomogram based on six RBP genes was established and internally validated in the TCGA cohort. Gene set enrichment analysis demonstrated some cancer-related phenotypes were significantly gathered in the high-risk group. Overall, our study first identified an RBP-related six-gene prognostic signature, which could serve as a promising prognostic biomarker and provide some potential therapeutic targets for HCC.

A robust nine-gene prognostic signature associated with tumour doubling time for hepatocellular carcinoma

BACKGROUND

Tumour doubling time (TDT) is an indicator reflecting tumour growth rate, however, the prognostic genes associated with the TDT in hepatocellular carcinoma (HCC) have not been fully identified.

MATERIALS AND METHODS

We obtained mRNA expression profiles and tumour doubling time from GSE54236 and used the Pearson correlation test to identify tumour doubling time-related genes (TDTRGs). We extracted TDTRGs from The Cancer Genome Atlas (TCGA) and identified prognostic genes using univariate Cox regression analysis and Kaplan-Meier survival analysis. Lasso and multivariate Cox regression analysis assisted in constructing the signature and International Cancer Genome Consortium (ICGC) served as an external validation.

RESULTS

We identified a total of 296 genes associated with tumour doubling time and developed a prognostic signature consisting of 9 genes. Patients were divided into high- and low-risk groups according to the uniform cutoff (0.85). Regardless of the clinical characteristics of the patients, the group at high risk exhibited obviously lower overall survival (OS) than did the group with low risk in both TCGA and ICGC cohorts. The prognostic model showed superior accuracy in both TCGA and ICGC cohorts, as confirmed by receiver operating characteristic (ROC) curve analysis. The univariate together with multivariate Cox regression analysis further suggested the ability of the signature to predict prognosis independently.

CONCLUSION

A novel prognostic signature for HCC was developed and validated in the study, which may be beneficial to improve the treatment strategy of HCC.

Construction and Validation of Novel Diagnostic and Prognostic DNA Methylation Signatures for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent life-threatening human cancers and the leading cause of cancer-related mortality, with increased global incidence within the last decade. Identification of effective diagnostic and prognostic biomarkers would enable reliable risk stratification and efficient screening of high-risk patients, thereby facilitating clinical decision-making. Herein, we performed a comprehensive, robust DNA methylation analysis based on genome-wide DNA methylation profiling. We constructed a diagnostic signature with five DNA methylation markers, which precisely distinguished HCC patients from normal controls. Cox regression and LASSO analysis were applied to construct a prognostic signature with four DNA methylation markers. A one-to-one correlation analysis was carried out between genes of the whole genome and our prognostic signature. Exploration of the biological function and the role of the underlying significantly correlated genes was conducted. A mixed dataset of 463 HCC patients and 253 normal controls, derived from six independent datasets, was used to valid the diagnostic signature. Results showed a specificity of 96.84% and sensitivity of 96.77%. Class scores for the diagnostic signature were significantly different between normal controls, individuals with liver diseases, and HCC patients. The present signature has the potential to serve as a biomarker to monitor health in normal controls. Additionally, HCC patients were successfully separated into low-risk and high-risk groups by the prognostic signature, with a better prognosis for patients in the low-risk group. Kaplan-Meier and ROC analysis confirmed that the prognostic signature performed well. We found eight of the top ten genes to positively correlate with risk scores of the prognostic signature, and to be involved in cell cycle regulation. This eight-gene panel also served as a prognostic signature. The robust evidence presented in this study therefore demonstrates the effectiveness of the prognostic signature. In summary, we constructed diagnostic and prognostic signatures, which have potential for use in diagnosis, surveillance, and prognostic prediction for HCC patients. Eight genes that were significantly and positively correlated with the prognostic signature were strongly associated with cell cycle processes. Therefore, the prognostic signature can be used as a guide by which to measure responsiveness to cell-cycle-targeting agents.

Genome-wide identification of CpG island methylator phenotype related gene signature as a novel prognostic biomarker of gastric cancer

Background

Gastric cancer (GC) is one of the most fatal cancers in the world. Results of previous studies on the association of the CpG island methylator phenotype (CIMP) with GC prognosis are conflicting and mainly based on selected CIMP markers. The current study attempted to comprehensively assess the association between CIMP status and GC survival and to develop a CIMP-related prognostic gene signature of GC.

Methods

We used a hierarchical clustering method based on 2,082 GC-related methylation sites to stratify GC patients from the cancer genome atlas into three different CIMP subgroups according to the CIMP status. Gene set enrichment analysis, tumor-infiltrating immune cells, and DNA somatic mutations analysis were conducted to reveal the genomic characteristics in different CIMP-related patients. Cox regression analysis and the least absolute shrinkage and selection operator were performed to develop a CIMP-related prognostic signature. Analyses involving a time-dependent receiver operating characteristic (ROC) curve and calibration plot were adopted to assess the performance of the prognostic signature.

Results

We found a positive relationship between CIMP and prognosis in GC. Gene set enrichment analysis indicated that cancer-progression-related pathways were enriched in the CIMP-L group. High abundances of CD8+ T cells and M1 macrophages were found in the CIMP-H group, meanwhile more plasma cells, regulatory T cells and CD4+ memory resting T cells were detected in the CIMP-L group. The CIMP-H group showed higher tumor mutation burden, more microsatellite instability-H, less lymph node metastasis, and more somatic mutations favoring survival. We then established a CIMP-related prognostic gene signature comprising six genes (CST6, SLC7A2, RAB3B, IGFBP1, VSTM2L and EVX2). The signature was capable of classifying patients into high‐and low‐risk groups with significant difference in overall survival (OS; p < 0.0001). To assess performance of the prognostic signature, the area under the ROC curve (AUC) for OS was calculated as 0.664 at 1 year, 0.704 at 3 years and 0.667 at 5 years. When compared with previously published gene-based signatures, our CIMP-related signature was comparable or better at predicting prognosis. A multivariate Cox regression analysis indicated the CIMP-related prognostic gene signature was an independent prognostic indicator of GC. In addition, Gene ontology analysis indicated that keratinocyte differentiation and epidermis development were enriched in the high-risk group.

Conclusion

Collectively, we described a positive association between CIMP status and prognosis in GC and proposed a CIMP-related gene signature as a promising prognostic biomarker for GC.

Downregulation of GNA14 in hepatocellular carcinoma indicates an unfavorable prognosis

Guanine nucleotide-binding protein subunit α14 (GNA14) knockdown was demonstrated to inhibit the proliferation of endometrial carcinoma cells in a recent study; however, its role in hepatocellular carcinoma (HCC) is unknown. In the present study, the clinical significance of GNA14 in HCC was assessed using a dataset of patients with HCC from The Cancer Genome Atlas database. The Integrative Molecular Database of Hepatocellular Carcinoma and Oncomine databases were also used to identify the expression levels of GNA14 in HCC tissues. The association between GNA14 expression levels and clinicopathological features was assessed using the Wilcoxon signed-rank test and logistic regression analysis. Kaplan-Meier curves and Cox regression analysis were applied to evaluate the independent risk factors for clinical outcomes. The present study determined GNA14 DNA methylation levels and tumor-infiltrating immune cells, as well as used Gene Set Enrichment Analysis (GSEA) in HCC. GNA14 mRNA expression levels were lower in HCC compared with normal tissues. Downregulation of GNA14 in HCC was significantly associated with tumor grade, clinical stage and T stage. Furthermore, low expression of GNA14 was an independent predictor for survival outcomes. GNA14 expression levels were partially correlated with the infiltration of B cells and macrophages. Additionally, GSEA analysis revealed that the expression levels of GNA14 were associated with multiple signaling pathways, such as translation, DNA replication, and homologous recombination. In conclusion, low GNA14 expression may be a novel biomarker for diagnosis and prognosis prediction for patients with HCC.

HPV-related methylation-based reclassification and risk stratification of cervical cancer

Human papillomavirus (HPV) is a clear etiology of cervical cancer (CC). However, the associations between HPV infection and DNA methylation have not been thoroughly investigated. Additionally, it remains unknown whether HPV‐related methylation signatures can identify subtypes of CC and stratify the prognosis of CC patients. DNA methylation profiles were obtained from The Cancer Genome Atlas to identify HPV‐related methylation sites. Unsupervised clustering analysis of HPV‐related methylation sites was performed to determine the different CC subtypes. CC patients were categorized into cluster 1 (Methylation‐H), cluster 2 (Methylation‐M), and cluster 3 (Methylation‐L). Compared to Methylation‐M and Methylation‐L, Methylation‐H exhibited a significantly improved overall survival (OS). Gene set enrichment analysis (GSEA) was conducted to investigate the functions that correlated with different CC subtypes. GSEA indicated that the hallmarks of tumors, including KRAS signaling, TNFα signaling via NF‐κB, inflammatory response, epithelial–mesenchymal transition, and interferon‐gamma response, were enriched in Methylation‐M and Methylation‐L. Based on mutation and copy number variation analyses, we found that aberrant mutations, amplifications, and deletions among the MYC, Notch, PI3K‐AKT, and RTK‐RAS pathways were most frequently detected in Methylation‐H. Additionally, mutations, amplifications, and deletions within the Hippo, PI3K‐AKT, and TGF‐β pathways were presented in Methylation‐M. Genes within the cell cycle, Notch, and Hippo pathways possessed aberrant mutations, amplifications, and deletions in Methylation‐L. Moreover, the analysis of tumor microenvironments revealed that Methylation‐H was characterized by a relatively low degree of immune cell infiltration. Finally, a prognostic signature based on six HPV‐related methylation sites was developed and validated. Our study revealed that CC patients could be classified into three heterogeneous clusters based on HPV‐related methylation signatures. Additionally, we derived a prognostic signature using six HPV‐related methylation sites that stratified the OS of patients with CC into high‐ and low‐risk groups.

Nudix hydrolase 1 is a prognostic biomarker in hepatocellular carcinoma

We investigated the prognostic significance of Nudix hydrolase 1 (NUDT1) in hepatocellular carcinoma (HCC). NUDT1 mRNA and protein levels were significantly higher in HCC tissues than normal liver tissues. The level of NUDT1 expression correlated with tumor grade, stage, size, differentiation, degree of vascular invasion, overall survival (OS), and disease-free survival (DFS) in HCC patients. Multivariate analysis showed that NUDT1 expression was an independent prognostic factor for OS and DFS in HCC patients. We constructed a prognostic nomogram with NUDT1 expression, AFP levels, vascular invasion, Child-Pugh classification, age, sex, AJCC staging, and tumor differentiation as variables. This nomogram was highly accurate in predicting the 5-year OS of HCC patients (c-index= 0.709; AUC= 0.740). NUDT1 silencing in HCC cells significantly reduced their survival, colony formation, migration, and invasiveness. Gene set enrichment analysis showed that biological pathways related to cell cycle, fatty acid metabolism, bile acid and bile salt metabolism, and PLK1 signaling were associated with NUDT1, as were the gene ontology terms "DNA binding transcription activator activity," "RNA polymerase II," "nuclear division," and "transmembrane transporter activity." Our study thus demonstrates that NUDT1 is a prognostic biomarker with therapeutic potential in HCC patients.

Transcriptomic profiling of peroxisome-related genes reveals a novel prognostic signature in hepatocellular carcinoma

Emerging evidence suggests that peroxisomes play a role in the regulation of tumorigenesis and cancer progression. However, the prognostic value of peroxisome-related genes has been rarely investigated. This study aimed to establish a peroxisome-related gene signature for overall survival (OS) prediction in patients with hepatocellular carcinoma (HCC). First, univariate Cox regression analysis was employed to identify prognostic peroxisome-related genes in The Cancer Genome Atlas liver cancer cohort, and least absolute shrinkage and selection operator Cox regression analysis was used to construct a 10-gene signature. The risk score based on the signature was positively correlated with poor prognosis (HR = 4.501, 95% CI = 3.021–6.705, P = 1.39e−13). Second, multivariate Cox regression incorporating additional characteristics revealed that the signature was an independent predictor. Time-dependent ROC curves demonstrated good performance of the signature in predicting the OS of HCC patients. The prognostic performance was validated using International Cancer Genome Consortium HCC cohort data. Gene set enrichment analysis revealed that the signature-related alterations in biological processes mainly involved peroxisomal functions. Finally, we developed a nomogram model based on the gene signature and TNM stage, which showed a superior prognostic power (C-index = 0.702). Thus, our study revealed a novel peroxisome-related gene signature that may help improve personalized OS prediction in HCC patients.